# R in Power BI

Embedding R scripts inside dashboards to reach statistical visualisations the native chart library does not offer.

**Rests on:** Power BI Visualization

*Data Engineering and Analytics · Data Visualization*


## Why learn this

You can create custom visualizations in Power BI that go beyond the standard buttons and menus. Standard tools show simple counts or totals well, but they struggle when you need to reveal hidden patterns in your data. For example, a standard bar chart shows you the average salary in a department, but it hides how much those salaries vary. By embedding R scripts, you can generate complex charts that reveal the 'shape' of your data—the range, the clusters, and the outliers—that a simple bar chart cannot show. You will be able to build dashboards that answer questions like: 'How are these two factors related?' or 'What is the typical spread of this metric?' using visualizations that standard Power BI does not provide out of the box.


## The intuition

Imagine you are building a report for your team. The default charts in Power BI—bars, lines, and pies—are excellent for basic comparisons. However, your manager asks for a view that shows the entire spread of salaries, not just the average. Or they want a chart that displays how many customers remain active over time, including those who have chipped away at the total. The built-in visuals might not do this clearly enough. Power BI has a feature that lets you run R scripts. R is a programming language designed for statistics and complex graphing. Instead of being stuck with drag-and-drop visuals, you can write a short script that creates exactly the chart you need. Power BI takes the data from your model, runs this script behind the scenes, and inserts the resulting image into your page as if it were a native visual. It is like having a specialist in the room who can draw any specific graph you describe, provided you give them the data and clear instructions.


## An analogy

Think of Power BI as a well-equipped kitchen. The native visuals are the standard pots and pans—reliable for everyday tasks like boiling water or frying an egg. But if you want to make a soufflé or a layered cake that requires precise chemical reactions and unique tools, you call in a guest chef. That chef is R. You give the chef your ingredients (the data from Power BI) and a recipe (the script). The chef goes to their own workspace, performs the complex preparation, and hands you the finished dish (the chart). The analogy holds because Power BI manages the dining room (the layout and data connection), while R handles the complex cooking. One limit of this analogy is that you cannot just call any professional chef; you must use the specific version of R built into Power BI, which has a restricted list of allowed tools (packages) and cannot interact directly with other chefs at the table.


## Definition

Using R code within Power BI to generate custom statistical charts that reveal data shapes, relationships, and trends not possible with standard drag-and-drop visuals.


## How it connects

This builds on your knowledge of Power BI Visualization, specifically how the underlying data structure determines what can be shown. It sits next to the practice of Data Storytelling, which is the art of explaining data insights clearly to an audience. While standard charts handle basic summaries, this technique allows you to create the detailed, exploratory graphics often needed in Exploratory Data Analysis, the process of investigating a dataset to discover patterns before formal analysis.


## The mathematics

This concept, at its core, is about using a statistical language (R) to create plots. The mathematics involved is not in the embedding mechanism itself, but in what you might want to plot. For example, a histogram, a boxplot, or a scatter plot with a regression line. The branch of mathematics this draws on is statistics and descriptive statistics—summarising a dataset with a few numbers and pictures. But we don't need to delve into complex formulas here; the R functions do the heavy lifting. What you need to understand is that a chart encodes data into visual elements: position, length, colour. When you write an R script, you are telling the computer how to map your data to those visual elements. For instance, a scatter plot maps one variable to the x-axis, another to the y-axis, and each dot is a data point. The relationship between the numbers and the visual is the mathematics of the chart, but you don't need to calculate it yourself—R does it.

No specific formula is required for embedding R in Power BI. The mathematical content is in the plotting functions you use, but they are built-in. For example, a simple linear regression formula: Sales = a + b*Month, but you don't need to write it manually; R's plot function can add a regression line.


## A worked example

```
Suppose you have a table of sales data in Power BI: Month, Region, and Sales. Months are 1 to 6, regions are 'North' and 'South'. Sales figures (in lakhs) for North: 12, 15, 14, 18, 20, 22. For South: 8, 10, 9, 12, 15, 17. You want a scatter plot of Month (x) vs Sales (y), coloured by Region.

Step 1: Step 1: In Power BI, add an R visual. The data pane shows a table with columns: Month (1,2,3,4,5,6), Region (North, North, ..., South), Sales (12,15,14,18,20,22,8,10,9,12,15,17)   # Power BI passes this data to the R engine as a data frame called 'dataset' by default.
Step 2: Step 2: Write R script: plot(x = dataset$Month, y = dataset$Sales, col = ifelse(dataset$Region == 'North', 'blue', 'red'))   # This uses R's base plotting function. It maps Month to the x-axis, Sales to the y-axis, and colours each dot blue for North, red for South.
Step 3: Step 3: The R engine runs the script. It produces a chart image with points at (1,12), (2,15), (3,14), (4,18), (5,20), (6,22) in blue, and (1,8), (2,10), (3,9), (4,12), (5,15), (6,17) in red.   # The plot is rendered and sent back to Power BI, which displays it in the visual placeholder.

Result: The scatter plot appears on the dashboard, showing the upward trend in sales over months, with North always higher than South. This is a bespoke statistical visual that the native scatter plot in Power BI can also do, but this illustrates the process. The real power comes with more complex scripts, like a custom histogram or a lattice of plots.
```


## From scratch, in Python

```python
Note: This is not about writing R code from scratch; it's about understanding the R script you embed. But we'll show a minimal R script that works in Power BI. In Power BI, when you add an R visual, the script editor opens with a template. You write:

# This is a comment
# 'dataset' is a data frame with the columns you selected
plot(dataset$Month, dataset$Sales,
     col = ifelse(dataset$Region == 'North', 'blue', 'red'),
     pch = 19,  # solid circle
     xlab = 'Month', ylab = 'Sales (lakhs)',
     main = 'Sales by Month and Region')

# You can also use ggplot2, but you need to install it in your local R environment (outside Power BI) and then it may be available.
# In Power BI, only the base R and a few packages are preinstalled. ggplot2 may not be available unless you have R installed on your machine and Power BI uses it.
# Actually, Power BI uses the R installation on your computer. So if you have R and ggplot2 installed, you can use it.

# Example with ggplot2 (if installed):
# library(ggplot2)
# ggplot(dataset, aes(x = Month, y = Sales, colour = Region)) + geom_point() + theme_minimal()
```


## The same thing, with a framework

```python
There's no direct Python framework equivalent for this concept, as it's about using R within Power BI. But if you were to mimic the intended functionality in Python, you might use matplotlib or seaborn to create similar plots. However, the integration with Power BI is unique to R. You could also use Python visual in Power BI, but that's a different feature.
```


## What the code does

The script starts with comments (lines starting with #) that are ignored by R. The first line calls plot(), which is R's basic plotting function. Inside it, we specify the x-values (dataset$Month), y-values (dataset$Sales), and a colour argument that uses ifelse() to assign blue if the Region is 'North' and red otherwise. The pch argument makes the points solid circles. Then we label the axes and give a title. When Power BI runs this, it takes the selected columns, creates a data frame called 'dataset', runs your script, and captures the plot output. If you use ggplot2, it's a more modern and flexible system, but it requires the package to be installed in your local R. Power BI will use your computer's R installation, so whatever packages you have installed there are available. However, for sharing reports, others need to have the same packages installed.


## Common misconceptions

**R visual is just for statisticians** — I need to be a statistician to use R in Power BI.

You only need to know a few R functions to create useful charts. You don't need a deep statistical background. In fact: R can be used by anyone willing to learn a few commands, and Power BI provides a script editor with examples.

**R visual is interactive like native visuals** — The R visual will respond to filters and cross-filtering like other Power BI visuals.

By default, R visuals are static images. They don't accept user interactions like tooltips or cross-filtering. In fact: You can add interactivity using the 'Power BI Visual' with R, but it's not automatic and requires extra setup.

**Any R package can be used** — I can install any R package in Power BI.

Power BI uses your local R installation, but it may restrict certain operations. Also, on Power BI Service, you have limited packages. In fact: In Power BI Desktop, you can use any package installed in your local R. For service, you need to use a certified list.

**R is faster than native visuals** — R visuals will make my dashboard slower.

R visuals can be slower because they execute an external script, and the chart is rendered as an image. In fact: Use R sparingly for complex visuals; for simple ones, native visuals are faster.

**R visual is a replacement for all charts** — I should use R for all my visuals to have more control.

Native visuals are easier to maintain, share, and interact with. In fact: Use R only when you need a chart that the built-in library cannot produce.

**The R script is stored in the PBIX** — If I share the report, others can run the R script.

If the report is shared via Power BI Service, the R engine may not be available, and the visual will not render. In fact: You need to ensure the service can run R, or export the visual as a static image.


## In short

R in Power BI lets you embed R scripts to create custom statistical visualisations that the native library lacks. It extends your dashboard's capabilities, but it's best used sparingly, only for visuals that are otherwise impossible. Remember: it requires a local R installation, packages must be available, and the visual is a static image by default.


## Where this is used

In the world of AI systems, R in Power BI may seem tangential, but it plays a role in the everyday applied machine learning workflow. For large language models (LLMs), this is rarely used directly, as LLMs are typically productionised with dashboards that use standard charts. However, in small models running on devices, there's no direct use—Power BI is a desktop/server reporting tool. In retrieval-augmented generation, one might use Power BI to monitor the performance of an RAG system. For agentic systems, dashboards are used to inspect logs and tool calls. But the real application is in data science reporting: a data scientist trains a model in Python, then shares results through a Power BI dashboard. They may embed an R script to create a model diagnostic plot, like a residual plot or an ROC curve. The mechanism: Power BI runs the R script on the dataset you've loaded. For example, you have a table of predicted vs actual values. You can write an R script that plots residuals and a Q-Q plot. The developer writes an R script, selects the columns for predicted and actual, and the visual appears. When done badly, the script might fail if the data types don't match, or the script is too slow. Best practice is to use it sparingly—only for the few visuals that native tools can't produce, as your library notes say. It's also constrained by the execution environment: you need R installed on the machine running Power BI, and the packages you use must be available. So for deployment across an organisation, this can be a bottleneck.


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Read this on the web: https://basavapurushottam.com/learn-ai/r-in-power-bi
